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ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level
We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464302/ https://www.ncbi.nlm.nih.gov/pubmed/37574757 http://dx.doi.org/10.1002/pro.4756 |
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author | Wilson, Colorado Lewis, Karen A. Fitzkee, Nicholas C. Hough, Loren E. Whitten, Steven T. |
author_facet | Wilson, Colorado Lewis, Karen A. Fitzkee, Nicholas C. Hough, Loren E. Whitten, Steven T. |
author_sort | Wilson, Colorado |
collection | PubMed |
description | We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase‐separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences. |
format | Online Article Text |
id | pubmed-10464302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104643022023-09-01 ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level Wilson, Colorado Lewis, Karen A. Fitzkee, Nicholas C. Hough, Loren E. Whitten, Steven T. Protein Sci Tools for Protein Science We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase‐separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences. John Wiley & Sons, Inc. 2023-09-01 /pmc/articles/PMC10464302/ /pubmed/37574757 http://dx.doi.org/10.1002/pro.4756 Text en © 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Tools for Protein Science Wilson, Colorado Lewis, Karen A. Fitzkee, Nicholas C. Hough, Loren E. Whitten, Steven T. ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title |
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title_full |
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title_fullStr |
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title_full_unstemmed |
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title_short |
ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level |
title_sort | parse 2.0: a web tool to identify drivers of protein phase separation at the proteome level |
topic | Tools for Protein Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464302/ https://www.ncbi.nlm.nih.gov/pubmed/37574757 http://dx.doi.org/10.1002/pro.4756 |
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